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Pinecone vs turbopuffer

Two managed vector store options for vector database. When each fits, what it costs, who moves from one to the other, and what makers who chose it say.

PineconeManaged vector storellms.txt
turbopufferManaged vector storellms.txt
Ask your AI about this, with this page as the source:ChatGPT ↗Claude ↗Perplexity ↗

Which fits you

Choose Pinecone if
  • You want a hosted index with nothing to operate

Use it whenYou want vector search without running any infrastructure.

Trade-offClosed source and cloud-only, so leaving means re-indexing somewhere else.

Choose turbopuffer if
  • You want a hosted index with nothing to operate

Use it whenYou have many separate namespaces, such as one index per customer.

Trade-offNo free tier (paid plans have a monthly minimum), and it's cloud-only.

At a glance

Pineconeturbopuffer
Used by55 makers' products · 23 open-source projects8 makers' products · 7 open-source projects
Cost at default usagevectors stored 1 million vectors, queries 1 million queries, vectors written or updated 500k writes$113/mo Standard$21/mo Launch
Downloads822.6k/wk−35% vs npm1.1M/wk3.9× vs npm
PricingFree Starter tier; Builder $20/month flat, Standard usage-based with a $50/month minimum, plus an Enterprise tier. · paid from $20/moUsage-based tiers with a monthly minimum; higher tiers add SSO, audit logs, and enterprise/BYOC deployments. · paid from $16/mo minimum usage
Free tierYesNo
Open sourceNoNo
Incidents, 90 daysfrom its status page9 (6 major)4 (4 major)

Cost as you grow

At 100k vectors Pinecone costs less ($0 vs $17); from about 500k vectors turbopuffer does ($19 vs $20).

$0$20,000$100,000$500,0000.10.5151050100
turbopufferPineconex: vectors stored (1,536 dimensions, about 6 gb per million) (million vectors), other usage scaled with it · cheapest usable plan at each point, list prices · try your own numbers
The numbers, plan by plan
Vectors stored (1,536 dimensions, about 6 GB per million)Pineconeturbopuffer
0.1$0 Starter$17 Launch
0.5$20 Builder$19 Launch
1$113 Standard$21 Launch
5$2,532 Standard$179 Launch
10$9,987 Standard$430 Launch
50$246,797 Standard$3,173 Launch
100$985,753 Standard$7,576 Scale

From each vendor's pricing page: Pinecone, turbopuffer.

What makers say

Makers on using it for vector database, from Product Hunt and Starter Story interviews, each linked to the source. Products with a page of their own and fuller notes first.

On Pinecone
TwelveLabs uses Pinecone to efficiently store and search the vector embeddings produced by our embedding model, enabling fast, scalable retrieval across large video and text datasets.
TwelveLabs, the makerSep 2026 ↗
Though we are using function tools to retrieve information from our integrations in real-time, we also use Pinecone to retrieve relevant long-term information.
Tometo, the makerSep 2026 ↗
We needed a vector database that's fast, reliable, and serverless for our RAG system. Pinecone was the easiest to set up and performs consistently at scale.
Starnus, the makerOct 2026 ↗
35 more on the Pinecone page →
On turbopuffer

No maker quote about turbopuffer for vector database yet.

Loved and watch-outs

Themes that recur in makers' words and Hacker News comments, each linked to what it summarises.

Pinecone
Most loved
  • Getting started is quick, with clear documentation and a generous free tier. PHPH 2PH 3PH 4
  • Handles large vector volumes with low latency as usage grows. PHPH 2
  • The serverless tier keeps costs down while iterating and at scale. PHPH 2PH 3
Watch-outs
  • It is one more database to run and sync, when Postgres or plain search often covers the need. HNHN 2HN 3HN 4
  • Its core features have become a commodity, and it was late to integrated embeddings compared with rivals. HNHN 2HN 3HN 4
  • It stores embeddings without the source chunk, unlike most other vector databases. HN
On Product Hunt: 4.9★, 74 reviews · mentioned most: ease of use, scalability, high-performance vector database
turbopuffer
Most loved
Watch-outs
  • There is no offline local emulator, so development and CI must hit the hosted service. HNHN 2HN 3
  • Its paid-only pricing floor is steep for small projects, which often stay on pgvector instead. HNHN 2

Who uses each

Pinecone55 makers' products
AI Context FlowBrev.ioClueoChatCustomGPTFleakHandit.aiHuman BehaviorInboxAgentsKickKipps AIKrooloKuse+43 more
turbopuffer8 makers' products
Mem0PlayerzeroPylonReadwisefalKeplerLegoraLinear

What makers pair each with

With Pinecone
LLM API
Frontend Framework
Transactional Email
Hosting
With turbopuffer
Transactional Email
Frontend Framework
LLM API
Database

Neither quite right?

See the whole vector database decision →
pgvectorInside your databaseApps already on Postgres that want vector search in the same database, joined with normal tables.
ChromaEmbedded / local-firstPrototyping RAG on your laptop with a pip or npm install and no server to run.vs Pinecone →vs turbopuffer →
QdrantOpen-source vector storeHeavy metadata filtering alongside vector search, self-hosted from one binary or on its managed cloud.vs Pinecone →vs turbopuffer →
WeaviateOpen-source vector storeHybrid search that mixes keyword and vector results, with built-in modules that can create embeddings for you.vs Pinecone →